[PPT] - Threats and Analysis Bruno Crpon J-PAL Course Overview 1. What is PowerPoint Presentation

SLIDE 1

Threats and Analysis

Bruno Crépon

J-PAL

SLIDE 2

Course Overview

1. What is Evaluation?
2. Outcomes, Impact, and Indicators
3. Why Randomize and Common Critiques
4. How to Randomize
5. Sampling and Sample Size
6. Threats and Analysis
7. Project from Start to Finish
8. Cost-Effectiveness Analysis and Scaling Up

SLIDE 3

Lecture Overview

A. Attrition
B. Spillovers
C. Partial Compliance and Sample Selection Bias
D. Intention to Treat & Treatment on Treated
E. Choice of outcomes
F. External validity
G. Conclusion

SLIDE 4

Lecture Overview

A. Attrition
B. Spillovers
C. Partial Compliance and Sample Selection Bias
D. Intention to Treat & Treatment on Treated
E. Choice of outcomes
F. External validity
G. Conclusion

SLIDE 5

Attrition

A. Is it a problem if some of the people in the

experiment vanish before you collect your data?

A. It is a problem if the type of people who disappear is

correlated with the treatment.

B. Why is it a problem?
A. Loose the key property of RCT: two identical

populations

C. Why should we expect this to happen?
A. Treatment may change incentives to participate in the

survey

SLIDE 6

Attrition bias: an example

A. The problem you want to address:

A. Some children don’t come to school because they are too weak (undernourished)

B. You start a school feeding program and want to do an evaluation

A. You have a treatment and a control group

C. Weak, stunted children start going to school more if they live next to a treatment school D. First impact of your program: increased enrollment. E. In addition, you want to measure the impact on child’s growth

A. Second outcome of interest: Weight of children

F. You go to all the schools (treatment and control) and measure everyone who is in school on a given day G. Will the treatment-control difference in weight be over-stated or understated?

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Before Treatment After Treament T C T C 20 20 22 20 25 25 27 25 30 30 32 30 Ave. Difference Difference

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Before Treatment After Treament T C T C 20 20 22 20 25 25 27 25 30 30 32 30 Ave. 25 25 27 25 Difference Difference 2

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What if only children > 21 Kg come to school?

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What if only children > 21 Kg come to school?

A. Will you underestimate

the impact?

B. Will you overestimate the

impact?

C. Neither
D. Ambiguous
E. Don’t know

Before Treatment After Treament T C T C 20 20 22 20 25 25 27 25 30 30 32 30

A. B. C. D. E.

23% 32% 23% 14% 9%

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Before Treatment After Treament T C T C [absent] [absent] 22 [absent] 25 25 27 25 30 30 32 30 Ave. 27,5 27,5 27 27,5 Difference Difference

0,5

What if only children > 21 Kg come to school absent the program?

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When is attrition not a problem?

A. When it is less than 25%

f the original sample

B. When it happens in the same proportion in both groups C. When it is correlated with treatment assignment

D. All of the above

E. None of the above

A. B. C. D. E.

5% 60% 25% 0% 10%

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Attrition Bias

A. Devote resources to tracking participants in the

experiment

B. If there is still attrition, check that it is not different in

treatment and control. Is that enough?

C. Good indication about validity of the first order

property of the RCT:

A. Compare outcomes of two populations that only differ

because one of them receive the program

D. Internal validity

SLIDE 14

Attrition Bias

A. If there is attrition but with the same response rate

between test and control groups. Is this a problem?

B. It can
C. Assume only 50% of people in the test group and 50% in

the control group answered the survey

D. The comparison you are doing is a relevant parameter of

the impact but… on the population of respondent

E. But what about the population of non respondent

A. You know nothing! B. Program impact can be very large on them,… or zero,… or negative!

F. External validity might be at risk

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Lecture Overview

A. Attrition
B. Spillovers
C. Partial Compliance and Sample Selection Bias
D. Intention to Treat & Treatment on Treated
E. Choice of outcomes
F. External validity
G. Conclusion

SLIDE 16

What else could go wrong?

Targe get t Popula lati tion

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Not in evaluation Evaluation Sample

Total al Popula lati tion

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Random Assignment Treatment Group Control Group

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Spillovers, contamination

Targe get t Popula lati tion

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Not in evaluation Evaluation Sample

Total al Popula lati tion

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Random Assignment Treatment Group Control Group

Treatment 

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Spillovers, contamination

Targe get t Popula lati tion

n

Not in evaluation Evaluation Sample

Total al Popula lati tion

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Random Assignment Treatment Group Control Group

Treatment 

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Example: Vaccination for chicken pox

A. Suppose you randomize chicken pox

vaccinations within schools

A. Suppose that prevents the transmission of disease,

what problems does this create for evaluation?

B. Suppose externalities are local? How can we

measure total impact?

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Externalities Within School Without Externalities School A Treated? Outcome Pupil 1 Yes no chicken pox Total in Treatment with chicken pox Pupil 2 No chicken pox Total in Control with chicken pox Pupil 3 Yes no chicken pox Pupil 4 No chicken pox Treament Effect Pupil 5 Yes no chicken pox Pupil 6 No chicken pox With Externalities Suppose, because prevalence is lower, some children are not re-infected with chicken pox School A Treated? Outcome Pupil 1 Yes no chicken pox Total in Treatment with chicken pox Pupil 2 No no chicken pox Total in Control with chicken pox Pupil 3 Yes no chicken pox Pupil 4 No chicken pox Treatment Effect Pupil 5 Yes no chicken pox Pupil 6 No chicken pox

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0% 100%

100%

0% 67%

67%

Externalities Within School Without Externalities School A Treated? Outcome Pupil 1 Yes no chicken pox Total in Treatment with chicken pox Pupil 2 No chicken pox Total in Control with chicken pox Pupil 3 Yes no chicken pox Pupil 4 No chicken pox Treament Effect Pupil 5 Yes no chicken pox Pupil 6 No chicken pox With Externalities Suppose, because prevalence is lower, some children are not re-infected with chicken pox School A Treated? Outcome Pupil 1 Yes no chicken pox Total in Treatment with chicken pox Pupil 2 No no chicken pox Total in Control with chicken pox Pupil 3 Yes no chicken pox Pupil 4 No chicken pox Treatment Effect Pupil 5 Yes no chicken pox Pupil 6 No chicken pox

SLIDE 22

How to measure program impact in the presence of spillovers?

A. Design the unit of randomization so that it

encompasses the spillovers

B. If we expect externalities that are all within

school:

A. Randomization at the level of the school allows for

estimation of the overall effect

SLIDE 23

Example: Price Information

A. Providing farmers with spot and futures price information

by mobile phone

B. Should we expect spillovers?
C. Randomize: individual or village level?
D. Village level randomization

A. Less statistical power B. “Purer control groups”

E. Individual level randomization

A. More statistical power (if spillovers small) B. But spillovers might bias the measure of impact

SLIDE 24

Example: Price Information

A. Actually can do both together!

B. Randomly assign villages into one of four groups, A, B and C C. Group A Villages

A. SMS price information to randomly selected 50% of individuals with phones B. Two random groups: Test A and Control A

D. Group B Villages

A. No SMS price information

E. Allow to measure the true effect of the program: Test A/B F. Allow also to measure the spillover effect: Control A/B

SLIDE 25

Lecture Overview

A. Attrition
B. Spillovers
C. Partial Compliance and Sample Selection Bias
D. Intention to Treat & Treatment on Treated
E. Choice of outcomes
F. External validity
G. Conclusion

SLIDE 26

Sample selection bias

A. Sample selection bias could arise if factors
ther than random assignment influence

program allocation

A. Even if intended allocation of program was

random, the actual allocation may not be

SLIDE 27

Sample selection bias

A. Individuals assigned to comparison group could

attempt to move into treatment group

A. School feeding program: parents could attempt to move

their children from comparison school to treatment school

B. Alternatively, individuals allocated to treatment group

may not receive treatment

A. School feeding program: some students assigned to

treatment schools bring and eat their own lunch anyway, or choose not to eat at all.

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Non compliers

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Targe get t Popula lati tion

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Not in evaluation Evaluation Sample Treatment group Participants No-Shows Control group Non- Participants Cross-overs Random Assignment

No! What can you do? Can you switch them?

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Non compliers

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Targe get t Popula lati tion

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Not in evaluation Evaluation Sample Treatment group Participants No-Shows Control group Non- Participants Cross-overs Random Assignment

No! What can you do? Can you drop them?

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Non compliers

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Targe get t Popula lati tion

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Not in evaluation Evaluation Sample Treatment group Participants No-Shows Control group Non- Participants Cross-overs Random Assignment

You can compare the

riginal groups

SLIDE 31

Lecture Overview

A. Attrition
B. Spillovers
C. Partial Compliance and Sample Selection Bias
D. Intention to Treat & Treatment on Treated
E. Choice of outcomes
F. External validity
G. Conclusion

SLIDE 32

ITT and ToT

A. Vaccination campaign in villages
B. Some people in treatment villages not treated
A. 78% of people assigned to receive treatment received some

treatment

C. What do you do?
A. Compare the beneficiaries and non-beneficiaries?

B. Why not?

SLIDE 33

Which groups can be compared ?

Assigned to Treatment Group: Vaccination Assigned to Control Group Acceptent : TREATED NON-TREATED Refusent : NON-TREATED

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What is the difference between the 2 random groups?

Assigned to Treatment Group Assigned to Control Group

1: treated – not infected 2: treated – not infected 3: treated – infected 5: non-treated – infected 6: non-treated – not infected 7: non-treated – infected 8: non-treated – infected 4: non-treated – infected

SLIDE 35

Intention to Treat - ITT

Assigned to Treatment Group(AT): 50% infected Assigned to Control Group(AC): 75% infected

Y(AT)= Average Outcome in AT Group
Y(AC)= Average Outcome in AC Group

ITT = Y(AT) - Y(AC)

ITT = 50% - 75% = -25 percentage points

SLIDE 36

Intention to Treat (ITT)

A. What does “intention to treat” measure?

“What happened to the average child who is in a treated school in this population?”

A. Is this difference a causal effect? Yes because

we compare two identical populations

B. But a causal effect of what?
A. Clearly not a measure of the vaccination
B. Actually a measure of the global impact of the

intervention

SLIDE 37

When is ITT useful?

A. May relate more to actual programs
B. For example, we may not be interested in the

medical effect of deworming treatment, but what would happen under an actual deworming program.

C. If students often miss school and therefore

don't get the deworming medicine, the intention to treat estimate may actually be most relevant.

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Wha hat t NOT T to to do do!

Intention School 1 to Treat ? Treated? Pupil 1 yes yes 4 Pupil 2 yes yes 4 Pupil 3 yes yes 4 Pupil 4 yes no Pupil 5 yes yes 4 Pupil 6 yes no 2 Pupil 7 yes no Pupil 8 yes yes 6 School 1: Pupil 9 yes yes 6

Avg. Change among Treated

(A) Pupil 10 yes no School 2:

Avg. Change among Treated A=
Avg. Change among not-treated

(B) School 2 A-B Pupil 1 no no 2 Pupil 2 no no 1 Pupil 3 no yes 3 Pupil 4 no no Pupil 5 no no Pupil 6 no yes 3 Pupil 7 no no Pupil 8 no no Pupil 9 no no Pupil 10 no no

Avg. Change among Not-Treated B=

Observed Change in weight

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Wha hat t NOT T to to do do!

3 3 0.9 2.1 0.9

Intention School 1 to Treat ? Treated? Pupil 1 yes yes 4 Pupil 2 yes yes 4 Pupil 3 yes yes 4 Pupil 4 yes no Pupil 5 yes yes 4 Pupil 6 yes no 2 Pupil 7 yes no Pupil 8 yes yes 6 School 1: Pupil 9 yes yes 6

Avg. Change among Treated

(A) Pupil 10 yes no School 2:

Avg. Change among Treated A=
Avg. Change among not-treated

(B) School 2 A-B Pupil 1 no no 2 Pupil 2 no no 1 Pupil 3 no yes 3 Pupil 4 no no Pupil 5 no no Pupil 6 no yes 3 Pupil 7 no no Pupil 8 no no Pupil 9 no no Pupil 10 no no

Avg. Change among Not-Treated B=

Observed Change in weight

SLIDE 40

From ITT to effect of Treatment On the Treated

A. What about the impact on those who received

the treatment? Treatment On the Treated (TOT)

A. Is it possible to measure this parameter?
A. The answer is yes

40

SLIDE 41

From ITT to effect of Treatment On the Treated (TOT)

A. The point is that if there is such imperfect

compliance, the comparison between those assigned to treatment and those assigned to control is smaller

B. But the difference in the probability of getting

treated is also smaller

C. The TOT parameter “corrects” the ITT,

scaling it up by this “take-up” difference

41

SLIDE 42

Estimating ToT from ITT: Wald

0.2 0.4 0.6 0.8 1 1.2 Assigned to Treatment Assigned to Control Green: Actually Treated

SLIDE 43

Interpreting ToT from ITT: Wald

0.2 0.4 0.6 0.8 1 1.2 Assigned to Treatment Assigned to Control Green: Actually Treated

SLIDE 44

Estimating TOT

A. What values do we need?
B. Y(AT) the average value over the Assigned to Treatment

group (AT)

C. Y(AC) the average value over the Assigned to Control

group (AC)

A. Prob[T|AT] = Proportion of treated in AT group
B. Prob[T|AC] = Proportion of treated in AC group
C. These proportion are called take-up of the program

SLIDE 45

Treatment on the treated (TOT)

A. Starting from a regression model

Yi=a+B.Ti+ei

A. Angrist and Pischke show

B=[E(Yi|Zi=1)-E(Yi|Zi=0)]/[P(Ti=1|Zi=1)-E(Ti=1|Zi=0)]

A. With Z=1 is assignement to treatment group

SLIDE 46

Treatment on the treated (TOT)

B=[E(Yi|Zi=1)-E(Yi|Zi=0)]/[P(Ti=1|Zi=1)-E(Ti=1|Zi=0)]

A. Estimates will be

[Y(AT)-Y(AC)]/[Prob[T|AT] -Prob[T|AC] ]

A. The ratio of the ITT estimates on the difference in

take-up

SLIDE 47

TOT estimate

Intention School 1 to Treat ? Treated? Pupil 1 yes yes 4 Pupil 2 yes yes 4 Pupil 3 yes yes 4 A = Gain if Treated Pupil 4 yes no B = Gain if not Treated Pupil 5 yes yes 4 Pupil 6 yes no 2 Pupil 7 yes no ToT Estimator: A-B Pupil 8 yes yes 6 Pupil 9 yes yes 6 Pupil 10 yes no A-B = Y(T)-Y(C)

Avg. Change Y(T)=

Avg. Change Y(C) =

A-B Observed Change in weight

SLIDE 48

TOT estimator

3

3 0.9 60% 20% 2.1 40%

0.9 5.25 Intention School 1 to Treat ? Treated? Pupil 1 yes yes 4 Pupil 2 yes yes 4 Pupil 3 yes yes 4 A = Gain if Treated Pupil 4 yes no B = Gain if not Treated Pupil 5 yes yes 4 Pupil 6 yes no 2 Pupil 7 yes no ToT Estimator: A-B Pupil 8 yes yes 6 Pupil 9 yes yes 6 Pupil 10 yes no A-B = Y(T)-Y(C)

Avg. Change Y(T)=

Avg. Change Y(C) =

A-B Observed Change in weight

SLIDE 49

Generalizing the ToT Approach: Instrumental Variables

1. First stage regression

T=a0+a1Z+Xc+u (a1 is the difference in take-up)

2. Get predicted value of treatment:

Pred(T|Z,X) = a0+a1Z+Xc

3. Perform the regression of Y on predicted

treatment instead on treatment Y=b0+b1Pred(T|Z,X)+Xd+v

SLIDE 50

Requirements for Instrumental Variables

A. First stage
A. Your experiment (or instrument) meaningfully

affects probability of treatment

B. Actually the experiment is “good” if there is a

large effect of assignment to treatment on treatment participation (the difference in take-up)

B. Exclusion restriction
A. Your experiment (or instrument) does not affect
utcomes through another channel

SLIDE 51

The ITT estimate will always be smaller (e.g., closer to zero) than the ToT estimate

A. True
B. False
C. Don’t Know

A. B. C.

0% 0% 0%

SLIDE 52

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Targe get t Popula lati tion

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Not in evaluation Evaluation Sample Assigned to Treatment group Treated Non treated Assigned to Control group No treated Random Assignment

TOT not always appropriate…

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TOT not always appropriate…

A. Example: send 50% of retired people in Paris a letter warning
f flu season, encourage them to get vaccines

B. Suppose 50% in treatment, 0% in control get vaccines C. Suppose incidence of flu in treated group drops 35% relative to control group

D. Is (.35) / (.5 – 0 ) = 70% the correct estimate?

E. What effect might letter alone have? F. Some retired people in the assignment to treatment group might consider it is better not to get a vaccine but… to stay home

G. They didn’t get the treatment but they have been

influenced by the letter

SLIDE 54

0.2 0.4 0.6 0.8 1 1.2 Assigned to Treatment Assigned to Control Green: Actually Treated

Non treated in the AT group impacted

SLIDE 55

Non treated in AT group do not cancel out

0.2 0.4 0.6 0.8 1 1.2 Assigned to Treatment Assigned to Control Green: Actually Treated

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Lecture Overview

A. Spillovers
B. Partial Compliance and Sample Selection Bias
C. Intention to Treat & Treatment on Treated
D. Choice of outcomes
E. External validity

SLIDE 57

Multiple outcomes

A. Can we look at various outcomes?
B. The more outcomes you look at, the higher the

chance you find at least one significantly affected by the program

A. Pre-specify outcomes of interest
B. Report results on all measured outcomes, even null

results

C. Correct statistical tests (Bonferroni)

SLIDE 58

Covariates

Rule: Report both “raw” differences and regression-adjusted results

A. Why include covariates?
A. May explain variation, improve statistical power
B. Why not include covariates?
A. Appearances of “specification searching”
C. What to control for?
A. If stratified randomization: add strata fixed

effects

B. Other covariates

SLIDE 59

Lecture Overview

A. Spillovers
B. Partial Compliance and Sample Selection Bias
C. Intention to Treat & Treatment on Treated
D. Choice of outcomes
E. External validity
F. Conclusion

SLIDE 60

Threat to external validity:

A. Behavioral responses to evaluations
B. Generalizability of results

SLIDE 61

Threat to external validity: Behavioral responses to evaluations

One limitation of evaluations is that the

evaluation itself may cause the treatment or comparison group to change its behavior

– Treatment group behavior changes: Hawthorne effect – Comparison group behavior changes: John Henry effect

Minimize salience of evaluation as much as

possible

Consider including controls who are measured at

end-line only

SLIDE 62

Generalizability of results

A. Depend on three factors:
A. Program Implementation: can it be replicated at a

large (national) scale?

B. Study Sample: is it representative?
C. Sensitivity of results: would a similar, but slightly

different program, have same impact?

SLIDE 63

Lecture Overview

A. Spillovers
B. Partial Compliance and Sample Selection Bias
C. Intention to Treat & Treatment on Treated
D. Choice of outcomes
E. External validity
F. Conclusion

SLIDE 64

Conclusion

A. There are many threats to the internal and external

validity of randomized evaluations…

B. …as are there for every other type of study
C. Randomized trials:
A. Facilitate simple and transparent analysis

A. Provide few “degrees of freedom” in data analysis (this is a good thing)

B. Allow clear tests of validity of experiment

SLIDE 65

Further resources

A. Using Randomization in Development

Economics Research: A Toolkit (Duflo, Glennerster, Kremer)

B. Mostly Harmless Econometrics (Angrist and

Pischke)

C. Identification and Estimation of Local Average